Summary of Batch Selection For Multi-label Classification Guided by Uncertainty and Dynamic Label Correlations, By Ao Zhou and Bin Liu and Jin Wang and Grigorios Tsoumakas
Batch Selection for Multi-Label Classification Guided by Uncertainty and Dynamic Label Correlations
by Ao Zhou, Bin Liu, Jin Wang, Grigorios Tsoumakas
First submitted to arxiv on: 21 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates how mini-batch construction affects the accuracy of deep neural networks during training, particularly in multi-label classification tasks. The authors find that selecting samples with higher uncertainty achieves better results than difficulty-based methods for single-label scenarios like binary and multi-class classification. However, existing batch selection methods for multi-label data do not leverage uncertainty information, which is crucial for optimizing performance. To address this gap, the authors propose an uncertainty-based multi-label batch selection algorithm that considers differences between successive predictions and the confidence of current outputs to assess label uncertainty. The method also incorporates dynamic uncertainty-based label correlations to emphasize instances whose uncertainty is synergistically expressed across multiple labels. Empirical studies demonstrate the effectiveness of this approach in improving performance and accelerating convergence for various multi-label deep learning models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how neural networks learn best when constructing mini-batches during training. They found that choosing samples with higher uncertainty helps them get better results, especially when there are many labels involved. But current methods don’t use this uncertainty information in the same way for multi-label tasks. To fix this, they came up with a new approach that looks at how predictions change over time and how confident the model is about its answers to decide which samples are most useful. This helps the network learn faster and do better on tasks like image classification. |
Keywords
» Artificial intelligence » Classification » Deep learning » Image classification
